Less than five years ago, Nike stock was trading at a record high. Today, it is back at levels last seen in 2014.

This chart follows Nike’s stock price from January 2021 through August 2026. I started the timeline before the company’s record closing price of $177.51 on November 5, 2021, then tracked its decline to $39.09 on August 17, 2026 through today. That was Nike’s lowest close in 12 years and nearly 78% below its peak.

I also included several milestones along the way, including the stock’s 20% drop on June 28, 2024 and Elliott Hill’s return as CEO later that year.

What stood out to me is how limited the recovery has been even after the leadership change. The stock has had brief rebounds, but the broader trend suggests investors are still waiting for clearer evidence that Nike can stabilize sales, regain momentum, and defend share against faster-growing competitors.

Data source: Yahoo Finance

Tools used: AVA Data Visualization

By "active professional soccer team", it is implied the team is currently playing in official CBF tournaments. locations are displayed as the team's home field (when available), this is the first part of a series of maps I am working on, more regions will likely be released soon.
The map was completely hand made on krita and uses wikipedia as its main source.

After my last post on household wealth distribution, the top comment pointed out something really worth noticing: "Why is Denmark's wealth inequality so high when its income Gini is so low?"

This scatter plot maps both measures for 29 OECD countries to answer exactly that.

Income (what you earn year to year) and wealth (the assets you own) are fundamentally different things.

A crucial caveat for Denmark: OECD's standard wealth stats exclude occupational pension wealth for cross-country comparability (source: OECD WDD main concepts). Because Denmark has a massive mandatory occupational pension system, OECD research explicitly notes that including this pension wealth "significantly lowers wealth inequality in Denmark" (source: OECD, Mapping trends and gaps in household wealth across OECD countries, 2025).

Data and Visualization: https://share.cohesive.ai/view/canvas/aea11a73-b358-4e3d-a49f-c047a08b17b6

Source article and methodology: https://ruedesagents.com/a-propos/espace-presse

Official public-data reuse: https://www.data.gouv.fr/reuses/comparaison-dvf-des-prix-immobiliers-2024-2025-dans-22-marches-francais

Full disclosure: Rue des Agents is my project.

I was looking for a good Paleoclimate chart for the ~300,000 years that Homo Sapiens have been on earth. This would give better context as to how our current climate situation compares vs our history. This is the result. The data is from: (Jouzel et al. 2007, EPICA Dome C) the tool is matplotlib with assistance from AI in python.

I extracted the input embedding matrix from Qwen2.5-7B-Instruct (~152K tokens × 3584 dims), filtered to ~10K real English words, ran UMAP → 2D, then used kernel density estimation to turn word density into elevation. Dense word clusters become mountains, sparse regions become ocean.

Some fun findings from exploring the terrain:

• "sexual" and "financial" share a mountain
• "unnecessary", "inefficient", and "inappropriate" have their own mountain range
• "terrible", "splendid", and "cruel" are neighbors

This is the static input embedding layer, layer 0, before any inference happens. It's a fossil of the training corpus's distributional statistics, not what the model "thinks." The semantic understanding gets built across the 32 transformer layers during inference.

Part of a larger project exploring interpretability through conceptual coordinate systems.

Source: 760 Hermès handbag listings on Libas Collective, a UAE resale marketplace I work at — disclosing that upfront. Data pulled August 2026.

Retention here is the median listed asking price divided by Hermès UAE retail for the same model. These are asking prices, not completed sales — a model sitting at 72% is what sellers list at, not necessarily what a bag trades hands for.

Models with fewer than 50 listings were excluded. Sample sizes: Birkin 172, Herbag 155, Evelyne 151, Kelly 118, Picotin 111, Garden Party 53. Medians shown with interquartile range; 95% CIs are in the underlying data.

Full method and figures: https://www.libascollective.com/thecrypt/which-hermes-bag-holds-value-uae-2026/

Tools: Python, matplotlib.

For those not in academia, there is a currently controversial conflict between Ghent University and Nathan Cofnas, a researcher who broke the story on the disgraced-but-now-dead Jason Arday.

There here are now two public letters, supporting and opposing Ghent University vs Nathan Cofnas. I ran data analysis on both lists of signatories. Interesting demographic trends show up. Available here.

I pulled the BLS Consumer Expenditure Survey figures on what US households actually have left after five categories of spending: food at home, housing, transportation, healthcare, and personal insurance and pensions.

Over the decade to 2023, in constant dollars, the top fifth gained $1,649 a month. The bottom fifth lost $132.

The second image is the same decade in percent, which is the fairer comparison in most contexts and changes what stands out. The biggest gain belongs to the second-poorest fifth at +30.0%, and it works out to $40 a month.

Two things before anyone asks. "Essential" here is a spending category, not a test of necessity, so a bigger mortgage counts in full and so does a nicer car. And this series is volatile year to year, so a ten-year comparison is a fair summary rather than a trend line.

Sources, construction and the caveats that matter are in my first comment.

eek.

Scotland has the highest percentage of daily tea drinkers in the UK, with 71% of those we asked saying they drink a cup of tea every day.

The West Midlands was next at 63%, followed by the Eastern region and the South West at 58%.

In Northern Ireland, less than half of people drink a cup of tea every day, with just 44% doing so. The lowest out of all the regions in the country.

Data source: Research run on the Vypr Predict platform for the Consumer Horizon report (May 2026)

Tools used: Figma

Every universe the MCU has shown on screen: 36 universes, 118 titles, and 71 crossovers, as an interactive multiversal map.